Table of contents
Gephi
Gephi is an open-source software for visualizing and analyzing large networks graphs.
As Gephi is an open source project, you can find more
open source alternatives and stats
on LibHunt.
User-friendly Interface
Gephi offers an intuitive and visually appealing interface that is relatively easy to navigate, even for beginners.
Interactive Visualization
Users can manipulate the visualization of networks in real-time, offering a hands-on approach to data analysis.
Extensive Plugins
Gephi supports a wide range of plugins that can extend its functionality, enabling users to customize their analysis and visualization needs.
High Performance
Designed to handle large graphs efficiently, Gephi can process, visualize, and manage extensive datasets without significant performance issues.
Open Source
Being open-source software, Gephi is freely available for anyone to use and modify, providing transparency and community-driven support.
Yes, Gephi is considered a good tool for network visualization and analysis. Its comprehensive feature set combined with its ease of use makes it a popular choice among researchers, analysts, and data scientists.
We have collected here some useful links to help you find out if Gephi is good.
Check the traffic stats of Gephi on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Gephi on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Gephi's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Gephi on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Gephi on Reddit. This can help you find out how popualr the product is and what people think about it.
Generating an HTML page is nice. But what if you want to cross-reference this data, render it yourself in a tool like Gephi, or even provide it as context to an LLM to audit your architecture? - Source: dev.to / 4 months ago
There are some tools for larger renderings. I've had success with Graphics but have you tried Gephi https://gephi.org/. - Source: Hacker News / almost 2 years ago
Load gexf file into Gephi and produce some dataviz by ourselves. - Source: dev.to / about 2 years ago
The following are not exactly what you have asked for. https://gephi.org/ This implements lots of graph visualization algorithms. https://strlen.com/treesheets/ Excel for tree data. - Source: Hacker News / over 2 years ago
๐งโ๐จ Enjoy some movie data art experience with Gephi and Runway. - Source: dev.to / over 2 years ago
Maybe try exporting your data from spiderfoot and use a graph tool like Gephi to import your data to and have it generate a graph for you. Source: over 3 years ago
Tool: custom python scripts and Gephi. Source: over 3 years ago
Datasource : https://developer.riotgames.com/apis Tool used : https://gephi.org Network Algorythm : Force Atlas 2. Source: over 3 years ago
Yeah the only way I've encountered uses Gephi to generate X and Y co-ordinates from your Excel source data. Since Tableau can't handle network graphs innately, it has to pretend it's plotting a dual-axis line and circle sheet. Source: over 3 years ago
Both Cytoscape and Gephi are options that you can try on Windows; both can run some classic community detection algorithms and can be extended with plugins. Personally, I'd recommend you to use igraph, which can be run as an R or python libraries. Then, about the specific algorithm, I have no experience on amino acid communities, but I would approach the issue thinking the properties that you would like to... Source: almost 4 years ago
I am searching for a GUI library that is capable of Visualizing and manipulating nodes in a Graph, Something like gephi (a social network analysis tool). Source: almost 4 years ago
Data sourced from the Minecraft Wikia. Data organized using Excel and visualized with Gephi. Source: almost 4 years ago
Two common GUI tools for analysis and editing of graph data are Gephi and Cytoscape. An older alternative starting with a P is Pajek, but I've never used it. Source: about 4 years ago
This really looks like it was made with gephi https://gephi.org/. Source: about 4 years ago
For data collection a Python program I wrote and to visualize it Gephi. Source: over 4 years ago
I analysed over 200k tweets and extracted the @ -Mentions within them to be able to generate a graph using networkx and displaying every node properly with the communities main name as a label using gephi. Source: over 4 years ago
I basically found the Spotify pages for each artist in my playlist, and then used the "related artists" feature to find out which pairs of artists were similar. I then exported this data into the software Gephi, which (with some tweaking) produced the graph in the post. Source: over 4 years ago
Rh/ github(reverted to the old version) I hate myself so much, I have to re-write 1week worth of stuff that I coded fuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuck (there won't be anything related to Gephi implementation and documentations related since it blew up. plus, very dirty code before cleanup). Source: over 4 years ago
To get an initial list of artists to use, I used my own YouTube playlist, getting a Spotify Id for each artist that has one. Next, I used Python and Spotifyโs API to retrieve 20 related artists (in order of similarity) for each original artist in the list. If any of the related artists were not in the original list, they were removed to maintain the initial amount of nodes. This data was then converted into a... Source: over 4 years ago
Tools used: Python3 (string, numpy, networkx, pandas) to process the data and build the network, and Gephi (https://gephi.org) to visualize it. Source: over 4 years ago
Corrected! Following rule#3 of this OC post: Data source: the texts of the novels downloaded from here https://github.com/dworschak/Witcher/tree/master/RESSOURCES/\_books Tools used: I used Python3 (string, numpy, networkx, pandas) to process the network ( and Gephi to visualize it (https://gephi.org) Short story: every node of the network corresponds to a Witcher character, and they are linked in they occurred... Source: over 4 years ago
Gephi is widely recognized as a powerful open-source tool for modeling, visually analyzing, and understanding complex network graphs. Given its comprehensive suite of features, it occupies a significant niche within the fields of data visualization and network analysis, positioning itself as a competitor to other solutions such as yEd, KeyLines, draw.io, PlantUML, LucidChart, OmniGraffle, and UMLGraph.
The general perception of Gephi is largely positive, particularly appreciated for its technical sophistication and user-friendly interface. Public opinions frequently highlight Gephi's ability to handle large datasets while maintaining detailed graphical displays. Users commend its interactive techniques for exploring and manipulating networks and its formidable set of algorithms for network layout, filtering, and statistical analysis. The Force Atlas 2 algorithm, in particular, is a cited strength due to its effective visualization of complex networks, making it popular among those dealing with massive or intricate datasets.
Gephi's free and open-source nature makes it an appealing choice for both professional and academic users. It frequently appears in technical discussions as a recommended tool for generating visualizations from GEXF files and other data formats. It supports an array of uses from social network mapping to examining interactions within systems, thanks to its flexible support for extensions and plugins.
The tool is widely utilized across various sectors and project types. Users often employ Gephi in academic research, particularly for analyzing social networks or large datasets, as indicated by its use in projects related to video game analytics, such as "League of Legends", and cultural studies like mapping music communities. It also finds utility in analyzing online community interactions, systemic structures in literature (e.g., "The Witcher" novels), and even cryptocurrencies' social media dynamics.
Numerous mentions of Gephi's application in conjunction with other programming tools like Python's networkx library indicate its role as an essential component in comprehensive data analysis workflows. Users appreciate its ability to integrate data processing scripts in Python and visualize complex data outputs effectively.
When compared to its competitors, Gephi is favored for its specialized focus on network graphs, setting it apart from tools like LucidChart or draw.io that provide more general diagramming capabilities. However, users sometimes seek alternatives or complement Gephi with other software like Cytoscape for specific functionalities like community detection in biological networks. The need for occasional manual adjustments and a learning curve for new users are minor points of critique.
Gephi's reputation as a robust network analysis tool is well-founded, sustained by a community of users who value its technical depth and diverse applications. It remains a tool of choice for those seeking an advanced approach to the visualization and analysis of complex datasets.
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Is Gephi good? This is an informative page that will help you find out. Moreover, you can review and discuss Gephi here. The primary details have not been verified within the last quarter, and they might be outdated. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.
